Senior Data Engineer

Roles & Responsibilities:
Build & Scale Enterprise Data Platform
Design, build, and operate a scalable enterprise data platform with reliable batch and real-time data pipelines to power analytics, AI, and business applications.
Leverage AI across the data engineering lifecycle to accelerate development, improve engineering productivity, and automate data quality, lineage, observability, governance, and platform monitoring.
Optimize platform performance, reliability, scalability, and cost through modern cloud-native architectures, automation, and engineering best practices.
Enable self-service data consumption through reusable data products, APIs, and governed datasets that empower Analytics, AI, and business teams.
AI/MLOps & Production AI Platform
Build and operate a scalable MLOps platform that supports the complete machine learning lifecycle—from feature engineering and model training to deployment, monitoring, and automated retraining.
Design, deploy, and manage production-grade model serving infrastructure for real-time and batch inference, ensuring high availability, low latency, scalability, and reliability.
Partner with Data Scientists and AI Engineers to productionize AI/ML solutions by implementing robust deployment pipelines, model governance, monitoring, and operational best practices.
QUALIFICATIONS, EXPERIENCE, & SKILLS
5 to 10 years of experience building data platforms using real-time as well as batch-processing big data solutions, including at least 2 years in a senior/lead engineering capacity.
Proficiency with data modeling, data warehousing, and building ETL/ELT pipelines at scale.
Exceptional programming skills in SQL, Python, Shell scripting, and Scala/PySpark.
Hands-on experience with AWS big data technologies (EC2, Redshift, S3, Glue, Kinesis and Lambda); exposure to other cloud platforms (Azure/GCP) is a plus.
Demonstrated experience using GenAI/LLM tools and frameworks (e.g., prompt engineering, RAG pipelines, LangChain/LlamaIndex, AI coding assistants such as Claude Code or GitHub Copilot, or building custom AI agents) to automate or accelerate engineering workflows.
Practical understanding of how to apply GenAI agents responsibly to repetitive data engineering tasks, with appropriate guardrails around data quality, security and governance.
Proficiency with Git/GitHub and modern CI/CD practices.
Experience with workflow orchestration tools (e.g., Airflow, dbt) and containerization (Docker/Kubernetes) is preferred.
Strong stakeholder management and the ability to translate business problems into scalable technical solutions.
Strong match with our value system & DNA.
We offer an opportunity to:
Help write next growth story for most successful QSR brand and helping become a leading food tech company in the world.
Build cutting edge, AI-augmented data solutions across a diverse set of problems and see the impact of your work immediately.
Elevate your core skills & build commercial acumen while working at the frontier of GenAI adoption in data engineering.
Work with & learn from exceptional talent in a fast-paced, entrepreneurial environment.

Work arrangement
No